Posts from the ‘Big Data’ Category

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The summer of 2015 marked the release of the blockbuster Sci-fi movie, “Terminator Genisys,” which grossed a record $350 million at the box office and further popularized the notion of time travel. In addition to sequels and prequels, Hollywood has now adopted plots for movies in which the audience can choose among alternate storylines and follow them to their logical conclusion. The future, as we know it, is plural. This year in our PreReview of 2015, we once again present a few alternative scenarios for the future from our vantage point at the end of 2014.

New business models created by emerging technologies and unforeseen partnerships dominated the headlines in 2015. Trending technologies such as the Internet of Things approached half the level of big data during 2015. Trending terms in the mainstream media such as drones and Bitcoin scored high in Google trends.

Here are three headlines from 2015 that caught our attention.

FedEx launches “parcelopter” service for 50-minute delivery

This is a premium delivery service powered by drones launched in San Francisco, New York and Los Angeles to deliver gifts for the 2015 holiday season. A TV ad campaign for Parcelopter shows a real-time broadcast of a recipient opening the package viewed by the sender on a smartphone a few thousand miles away. It also shows a logistics team at FedEx headquarters in Memphis, Tennessee, which includes 20 drone pilots coordinating deliveries. FedEx is expected to parlay its holiday season experience and associated data to gain competitive advantage in its logistics business.

Drone cameras that can capture images at resolution of 2 cm are widely projected to replace satellite imagery (with 30 cm resolution) as a source of geospatial data in the future. FedEx field-tested the operations of drone delivery in downtown Manhattan for emergency medical supplies for seven hospitals in October, soon after the Federal Aviation Administration (FAA) released a set of regulatory guidelines for the commercial use of drones. Most industry analysts had expected that the FAA would miss the congressional deadline to release commercial drone guidelines in September and were unprepared for the holiday season. Google Express, AmazonFresh and Starbucks are expected to release their version of Parcelopter in 2016.

50 million Apple Watches sold

Sales of the Apple Watch have surpassed some of the most optimistic analyst forecasts for 2015. Critics had pronounced the product as unsuitable for both millennials, who do not use watches, as well as for baby boomers, who prefer a larger form factor. As it turns out, the killer app for the Apple Watch was not to tell time. Apple’s partnership with United HealthCare, Humana and Kaiser Permanente led to popularity of the Apple Watch as a health-monitoring device. Leading insurers are getting ready to announce a variable discount on premiums linked to physical activity.

In a related story, Apple denied that it approached Rolex to create a luxury brand of the Apple Watch. Earlier in 2015, luxury goods retailers that had stocked an 18K gold version of the Apple Watch have since dropped the product from their line. Meanwhile, speculation is rife about Apple buying a bitcoin company in 2016. Could this be the killer app for Apple Watch in 2016?

In-Stadium mobile ads for Super Bowl 50 will generate $20 million

Levi’s stadium in Santa Clara will host the first Super Bowl with the largest digital footprint in the history of the event. Infrastructure at the stadium for this event is being designed to serve 5 TB of data over 1,200 Wi-Fi hubs to 70,000 fans in the stadium during the game on February 7, 2016. Peak bandwidth usage is expected to reach 5 Gbps.

Advertisers set a price of $100,000 for a 10-second mobile ad spot. A mobile app will also deliver the advertised products from the concessions stands to one of 70,000 seats in the stadium. PepsiCo is rumored to have allocated $1 million to in-stadium advertising for the event.

NFL authorities are also in active discussion with Google, Facebook, Twitter and Apple to crowdsource real-time sentiments about the game from sports fans through a series of mobile apps. Meanwhile, the most popular app for Levi’s stadium to date is Pnow. This app uses a predictive model to find the best time to use the restrooms based on real-time data feeds and historical data from the past 49 Super Bowl games.

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Game on….I think we just witnessed a big next generation big data leap in Healthcare Wellness Data and Analytics. Apple jumped into the health information business on June 2 2014, launching both a new health app (Health) and a cloud-based health information platform with IOS 8 (HealthKit). This was followed by the Watch launch in September 10, 2014, an intelligent health and fitness companion.

Google followed with Google Fit on June 25. Fit is a set of APIs that will allow developers to sync data across wearables and devices. Google Fit is the equivalent of Apple’s HealthKit. Google didn’t announce an equivalent of Apple Health app. It is expecting its ecosystem of Android partners to innovate with apps. Google also might be taking a different approach with Fit aligned with Android Wear SDK which extends the Android platform to a new generation of wearable devices.

Understanding Health, Wellness and HealthKit

What is the end game? First is that many healthcare companies are trying to change patients’ behavior to improve outcomes with data:

being more proactive and taking a preventative approach

trying to build relationships with patients and engage them more in their own care

trying to get them to adopt healthier behaviors and make better choices

The goal is collect real-time biometric data to feed EMR for clinical care management and CRM for outreach and engagement.

Apple’s mobile App, called “Health”, will collect an enormous of realtime data in the form of number of body metrics including blood pressure, heart rate, and stats on diet and exercise. Data is collected about personalized daily fitness goals from either motion sensors in phones or next generation wearables like the Watch. Health will constantly monitor key health metrics (like blood sugar for diabetics or blood pressure), and if any of them begin to move outside the healthy range, the app can send a notification to the user or a surrogate like user’s doctor. What Apple does well as usual is providing simple, easy-to-use dashboards consumed via mobile apps for health and fitness (see below).

The mobile Health app will share all its information with a new cloud platform called “HealthKit.” The new health cloud platform is designed to act as a global repository for all the user’s health information. It will accept data collected by a variety of third-party devices and apps. For instance Nike is now working to makes its health and fitness apps integrate with HealthKit.

HealthKit cloud-based platform is where the real heart of the operation is. It uses real-time and historical data, aggregating data from all the devices tracking vital statistics. HealthKit will allow consumers to know more about their body and track every step. HealthKit also allows consumers to share data with healthcare Providers and even Payors. HealthKit also takes a big burden off developers who no longer have to build custom tools and various API interfaces to transfer, sync and collate health data. This way the developers can focus on value add apps like visualization, interpretation/analysis rather than plumbing like data security, permission gathering.

Patient-centered, consumer-driven, and value-based Business Models

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The old playbook no longer works. Everyone acknowledges that U.S healthcare is broken.

Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.) will reinvent healthcare as we know it. I expect the healthcare transformation to start incrementally and develop slowly in sophistication. Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.

Why change? Consider this:

Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.

Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees. Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.

Historically, employers like Honeywell, Wal-Mart and their employees have not had access to comprehensive information about the cost and quality of care as they evaluate benefit designs across multiple health plans and treatment options.

In some cases, U.S health care providers and other market participants have actively resisted efforts by employers and others to obtain information about the costs and quality of health care services. Why? because opaqueness means money. UCSF researchers uncovered an enormous discrepancy in what different hospitals charge for the same procedure, ranging from a low of $1,529 to a high of $183,000. The median hospital charge was $33,611. The startling cost variation illustrates an inefficient system.

Despite this resistance, the health care industry generates extensive data that is relevant to determining the cost and quality of health care services. These data reside in myriad formats and disparate databases, without a common infrastructure, and have therefore been of limited value to employers and employees in controlling costs and improving outcomes.

In many cases, information relating to health care services has restrictions on its use, such as contractual agreements that some health plans and providers have historically entered into to not disclose price information. These factors make it challenging for employers and employees to use these data for the purposes of measuring cost and quality and making informed decisions. Read more

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Healthcare Benefits are the 2nd costliest line item for companies in the U.S. So, companies are taking aggressive steps to reduce this spend. Consider this:

IBM is moving to a private health exchange…Extend Health private exchange will be handling plan options for 110,000 IBM retirees

Walgreens is moving employees to a Corporate Health Exchange. Of the 180,000 Walgreen employees eligible for healthcare insurance, 120,000 opted for coverage for themselves and 40,000 family members. Another 60,000 employees, many of them working part-time, were not eligible for health insurance.

Trader Joe’s — decided to send some employees to the new public exchanges. Trader Joe’s has left coverage for three-quarters of its work force untouched but is giving part-time workers a contribution of $500 to buy policies. Because of the employees’ low incomes, the company says it believes many will be eligible for federal subsidies to help them afford coverage.

Time Warner will direct retirees to an exchange to get health coverage

For the past year I have done strategy and implementation work in the employee Healthcare benefits and Private Exchange area. I wanted to share my insights into the massive structural changes taking place in health insurance. The move to patient-centered, consumer-driven, and value-based models is real.

Employee Health insurance in the U.S. is at the cusp of a major transition from an employer-driven payor model to a model directly involving many more employees and consumers. Private health insurance exchanges with a defined contribution approach represent a significant step in this journey. Also some clever risk shifting strategies are emerging where employers are moving part-time workers onto public exchanges.

The market size is enormous. Healthcare spending is forecasted to be ~$3.1 trillion in 2014, with $620 Bln of this paid by U.S. employers. In 2013, employers contributed 32% more in health care expenses than 2008.

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A satisfying customer experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest personalization strategy leveraging wearable computing (and analytics) to track, measure and improve the overall park experience. The ultimate goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.

Wearable computing seems to be the next big thing. Many believe a new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.

Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate data.

What most people don’t understand is how powerful wearables (coupled with analytics) can be in designing new user experiences. Businesses thrive when they engage customers by creating a longitudinal view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.

Wearable Computing at Disney: MyMagic+

Disney has been rolling out a new guest experience called MyMagic+ to the 30 million guests per year at the Walt Disney World Resort in Orlando.

Realizing that guests were arriving with smartphones and tablets in hand and expecting access to more information, Disney started the MyMagic+ initiative to provide a next generation experience. The overarching goal of MyMagic+ is to provide a much more personalized friction-free vacation at various theme parks, even down to characters knowing your name.

Disney is following in the steps of Harrah’s (now Caesars Entertainment) Total Rewards program that provided an integrated experience for gamblers across nearly 40 resorts and casinos. Loyal spenders were rewarded with innumerable entertainment options, enticing special offers, free hotel rooms, and different ways to redeem credits.

How does MyMagic+ work?

A key element of MyMagic+ is MagicBand. MagicBands is a ultra-personalization experience. These brightly colored bands link with online profiles for each visiting family member, and can be scanned at park kiosks to access advance ride bookings, receive customer service, and pay for all the stuff your kids want to buy.

The key to a great experience is being predictive in terms of context. For instance, while wearing her MagicBand, a young lady who loves Disney princesses might be approached by her favorite of the park’s life-size characters and be greeted by name.

Disney extracts and integrates all the information about the guest from all the park siloed data systems. as well as from external sources. This allows them to create a longitudinal view of each guest’s behavior over channels, activities and time.

Sophisticated pattern-detection science is applied against the 360-degree view to extract each guest’s behavioral predictors – like early warning on guest/family fading, real-time park experience dynamics (via feedback), and each guest sensitivity to specific promotions. The objective is to turn these signals into individuated recommendations served via customer marketing systems.

Technology behind MagicBand

According to Disney, each waterproof MagicBand contains an HF Radio Frequency device and a transmitter which sends and receives RF signals through a small antenna inside the MagicBand and enables it to be detected at short-range touch points throughout Walt Disney World Resort. MagicBands can also be read by long-range readers and used to deliver personalized experiences, as well as provide information that helps us improve the overall experience.

The next version of MagicBand might have much more computing built into it. If they go the Android route…Google has announced an SDK aimed at making Android, more palatable for small devices. Android apparently was consuming more battery. Samsung tried using Android for the Galaxy Gear, its smart watch, and the results were not so great. It couldn’t last very long without a recharge. For the Gear 2 Samsung dropped Android in favor of Tizen, its own operating system. I won’t be surprised if Apple and Disney team up in a few years around this.

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In Memory Data Grid (IMGD) is a data structure that is being increasingly cited as a solution to the problem of scaling big data applications. Unlike in-memory applications, IMGDs distribute only the data across RAM over multiple servers. With memory prices continuing to fall and the volume of data for an application continuing to rise, solutions based on memory are looking more attractive to manage the performance bottlenecks of applications using Big Data. Should IMGD be on your radar screen for a Big Data application?

In order to understand this and other questions on IMGDs, Carpe Datum Rx spoke to Miko Matsumura, VP of Marketing and Developer Relations at Hazelcast, who has seen recent adoption of this technology in banks, telcos and technology companies. Here is an extract from our discussion.

Why is it so important to distribute data in a data grid? Why should it be In-memory?

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Another day, another data breach. Just received another “We’re sorry you got hacked”…letter.

This is the fifth letter I have received in the past 3 months: Forbes.com, Target, Neiman Marcus, credit card company and a previous employer. What is going on?

Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify? Why is the attack signature – sophisticated, self-concealing malware – so difficult to spot? Do firms need to invest in NSA PRISM type threat monitoring capabilities?

Obviously… where there is pain…there is opportunity for entrepreneurs see below – data from IBM). There is a growing focus on big data use case for security analytics after all the breaches we are seeing. General Electric announced it had completed a deal to buy Wurldtech, a Vancouver-based cyber-security firm that protects big industrial sites like refineries and power plants from cyber attacks.

Here are three recent examples that I was personally affected by – Forbes, Target, Neiman Marcus.

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In the movie “Minority Report,” set in 2054, Tom Cruise plays the captain of the “PreCrime” police force, which uses “precognitive” abilities of mutants to stop crime before it happens. Silicon Valley futurists have sometimes used this reference in the context of the art of the possible with Big Data. We have another 40 years to go to see how analytics can accurately forecast future events based on human behavior. Meanwhile, imagining the future with some level of accuracy is within our reach today.

Value creation in the data economy made headlines in 2014. While Big Data continued to be the buzzword of the year in 2014, solutions that created economic impact were center stage. Trending terms such as “predictive analytics” and “advanced analytics” approached the levels of “Big Data” on Google Trends during the year. “ROI,” which was vaguely referenced in the last two years, became the most commonly used term with Big Data in 2014. Here is a cross-section of 2014 events.

This is their next-generation TV appliance that integrates social media engagement with the TV watching experience. Earlier in 2013, Apple acquired Topsy Labs, a reseller for Twitter content for $200M. This was followed by a series of less publicized acquisitions of social media data companies. Apple is characteristically tight-lipped about its plans for monetizing this product with advertising, but speculation is rife that Apple is poised to get a piece of the $600 billion that is spent on advertising today.

These eight themes – through product or business innovation – Goldman claims are poised to transform addressable markets or open up entirely new ones, offering growth insulated from the broader macro environment and creating value for their stakeholders.

Goldman focuses on the impact of creative destruction – a term made famous by the Austrian economist Joseph Schumpeter, which emphasized the fact that innovation constantly drives breeding of new leaders and replacement of the old.

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Health expenditures in the United States crossed $3.0 trillion in 2013 which is more than ten times the $256 billion spent in 1980.

Almost 15% of U.S GDP is spent on healthcare…a staggering number. As a mega-vertical, healthcare covers several major segments (the 7 Ps)

Payers (Health Insurance and Health Plans),

Providers (Hospital Systems, Labs and IDNs),

Pharmacy (retail distribution networks), and

Pharmaceutical and medical equipment manufacturers,

Prescribers (Physicians, clinics and pharmacy minute clinics)

Police (Regulators, FDA)

Patients (consumers)

A Healthcare system is a complex beast and difficult to navigate – providers need to make it easier for patients. They are using people resources like care coordinators and patient navigators to help patients navigate the system.

The focus on the payor side is in digitizing Health today is to reduce the amount of waste in the health care system via implementation of new forms of health IT and Analytics… that reduces inefficiencies, redundancies and administrative costs.

According the CEO of Aetna…”the health care system wastes more than $765 billion each year – that’s 30 percent of our health care spending.”

While spending on health care is dominating headlines, the health care industry (7Ps) is in a state of flux. Stakeholders across the health care sector are running hard to reduce costs. The drivers impacting cost of healthcare include:

Aging population – Patient history and patterns of care impacting patient readmission rates

In this posting we look at Digital Health Care use cases and how data and analytics are being slowly but sure being adopted in the form of informatics. All this change is being driven under the guise of Health Reform.

Defining Business Analytics

What is Business Analytics? Business Analytics is the intersection of business and technology, offering new opportunities for a competitive advantage. Business analytics unlocks the predictive potential of data analysis to improve financial performance, strategic management, and operational efficiency.

What is BI? BI is the "computer-based techniques used in spotting, digging-out, and analyzing 'hard' business data, such as sales revenue by products or departments or associated costs and incomes. Objectives of BI implementations include (1) understanding of a firm's internal and external strengths and weaknesses, (2) understanding of the relationship between different data for better decision making, (3) detection of opportunities for innovation, and (4) cost reduction and optimal deployment of resources." (Business Dictionary). Most widely used BI tool is Microsoft Excel.
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What is Big Data? Big data refer to data scenarios that grow so large (petabytes and more) that they become awkward to work with using traditional database management tools. The challenge stems from data volume + flow velocity + noise to signal conversion. Big data is spawning new tools that are mix of significant processing power, parallelism and statistical, machine learning, or pattern recognition techniques
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Corporate performance management software and performance management concepts, such as the balanced scorecard, enable organizations to measure business results and track their progress against business goals in order to improve financial performance.
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Data visualization tools, include mashups, executive dashboards, performance scorecards and other data visualization technology, is becoming a major category.
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BI platforms provide a range of capabilities for building analytical applications. Examples are Oracle OBIEE, SAP Business Objects 4.0. There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search. The idea of standardizing on one supplier for all of one’s BI capabilities is difficult to do. Increasingly, standardization and more about managing a portfolio of tools used for a set of capabilities and use cases.
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Data integration tools and architectures in support of BI continue to evolve. Extract-Transfer-Load (ETL) tools make up a big segment of this category in addition to data mapping tools. Organizations must now support a range of delivery styles, latencies, and formats.
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BI is about "sense and respond." Analytics is about "anticipate and shape" models.

About

Business Analytics 3.0 blog is meant for decision makers and managers who are trying to make sense of the rapidly changing technology landscape and build next generation solutions. It is aimed at helping business decision makers navigate the "Raw Data -> Aggregate Data -> Intelligence -> Insight -> Decisions" chain.